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ABSTRACT:

SUBMITTER: Wang M

PROVIDER: S-EPMC7236533 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Mass spectrometry searches using MASST.

Wang Mingxun M Jarmusch Alan K AK Vargas Fernando F Aksenov Alexander A AA Gauglitz Julia M JM Weldon Kelly K Petras Daniel D da Silva Ricardo R Quinn Robert R Melnik Alexey V AV van der Hooft Justin J J JJJ Caraballo-Rodríguez Andrés Mauricio AM Nothias Louis Felix LF Aceves Christine M CM Panitchpakdi Morgan M Brown Elizabeth E Di Ottavio Francesca F Sikora Nicole N Elijah Emmanuel O EO Labarta-Bajo Lara L Gentry Emily C EC Shalapour Shabnam S Kyle Kathleen E KE Puckett Sara P SP Watrous Jeramie D JD Carpenter Carolina S CS Bouslimani Amina A Ernst Madeleine M Swafford Austin D AD Zúñiga Elina I EI Balunas Marcy J MJ Klassen Jonathan L JL Loomba Rohit R Knight Rob R Bandeira Nuno N Dorrestein Pieter C PC

Nature biotechnology 20200101 1

PMID: 31894142

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Project description:Transcriptomic studies have attempted to classify glioblastoma (GB) into subtypes that predict survival and that have different therapeutic vulnerabilities. Here we identified three metabolic subtypes: glycolytic, oxidative and a mixed glycolytic/oxidative by mass spectrometry imaging of rapidly excised tumour sections from two GB patients infused with [U-13C]glucose and from spatial transcriptomic analysis of contiguous sections. The phenotypes did not correlate with microenvironmental features, including proliferation rate, immune cell infiltration, and vascularisation, were retained when patient-derived cells were grown in vitro, or as orthotopically implanted xenografts, and were robust to changes in oxygen concentration, demonstrating their cell intrinsic nature. The spatial extent of the regions occupied by cells displaying these distinct metabolic phenotypes are large enough to be detected using clinically applicable metabolic imaging techniques. A limitation of the study is that it is based on only two patient tumours, albeit on multiple sections, and therefore represents a proof-of-concept study. Glioblastoma is the most common primary adult brain cancer. Transcriptomic analyses have attempted to classify GB into subtypes that could predict treatment response, with a recent study that used a pathway-based classification defining metabolism-associated subtypes with distinct therapeutic vulnerabilities. These included a mitochondrial subtype, which is associated with a more favourable clinical outcome and that is sensitive to inhibitors of oxidative phosphorylation, and a glycolytic/plurimetabolic subtype that is resistant to multiple treatment types. An important question is the extent to which the metabolism displayed by tumour cells in vivo are cell intrinsic and how much they are defined by the tumour microenvironment. We have addressed this question using mass spectrometry imaging of rapidly excised tumour sections from GB patients infused with [U-13C]glucose immediately prior to surgery to image tumour cell metabolic fluxes in vivo and from a spatial transcriptomic analysis of adjacent sections. To our knowledge, this is the first report demonstrating high resolution imaging of metabolic fluxes in a human tumour in vivo.

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Mass spectrometry searches using MASST.

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Mass spectrometry searches using MASST.

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